Heavy class imbalance means that a default threshold of \(P=0.5\)  for each Sigmoid may be too insensitive. Rather than adjusting the model directly via the cost function, or by data resampling, we chose the pragmatic approach of adjusting the decision threshold. This assumes that the underlying representation, that is, the decision surface, is accurate. Kang et al. have previously argued that accurate representation is possible in problems, like ours, with class imbalance [1]}.
